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AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents

Jiaxiang Chen, Jingwei Shi, Lei Gan, Jiale Zhang, Qingyu Zhang, Dongqian Zhang, Xin Pang, Zhucong Li, Yinghui Xu

TL;DR

AI2Agent tackles the bottleneck of deploying AI projects across diverse environments by turning projects into autonomous agents. It combines guideline-driven execution, self-adaptive debugging, and case-and-solution accumulation, backed by a knowledge repository and retrieval-augmented guidance. The method is validated on 30 AI deployment cases, showing substantial reductions in deployment time and improvements in success rates. The work enables standardized, modular, and scalable AI deployments with reduced human intervention, and the authors publish code and a demo video.

Abstract

As AI technology advances, it is driving innovation across industries, increasing the demand for scalable AI project deployment. However, deployment remains a critical challenge due to complex environment configurations, dependency conflicts, cross-platform adaptation, and debugging difficulties, which hinder automation and adoption. This paper introduces AI2Agent, an end-to-end framework that automates AI project deployment through guideline-driven execution, self-adaptive debugging, and case \& solution accumulation. AI2Agent dynamically analyzes deployment challenges, learns from past cases, and iteratively refines its approach, significantly reducing human intervention. To evaluate its effectiveness, we conducted experiments on 30 AI deployment cases, covering TTS, text-to-image generation, image editing, and other AI applications. Results show that AI2Agent significantly reduces deployment time and improves success rates. The code and demo video are now publicly accessible.

AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents

TL;DR

AI2Agent tackles the bottleneck of deploying AI projects across diverse environments by turning projects into autonomous agents. It combines guideline-driven execution, self-adaptive debugging, and case-and-solution accumulation, backed by a knowledge repository and retrieval-augmented guidance. The method is validated on 30 AI deployment cases, showing substantial reductions in deployment time and improvements in success rates. The work enables standardized, modular, and scalable AI deployments with reduced human intervention, and the authors publish code and a demo video.

Abstract

As AI technology advances, it is driving innovation across industries, increasing the demand for scalable AI project deployment. However, deployment remains a critical challenge due to complex environment configurations, dependency conflicts, cross-platform adaptation, and debugging difficulties, which hinder automation and adoption. This paper introduces AI2Agent, an end-to-end framework that automates AI project deployment through guideline-driven execution, self-adaptive debugging, and case \& solution accumulation. AI2Agent dynamically analyzes deployment challenges, learns from past cases, and iteratively refines its approach, significantly reducing human intervention. To evaluate its effectiveness, we conducted experiments on 30 AI deployment cases, covering TTS, text-to-image generation, image editing, and other AI applications. Results show that AI2Agent significantly reduces deployment time and improves success rates. The code and demo video are now publicly accessible.

Paper Structure

This paper contains 19 sections, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. DevOps Paradigm
  4. LLM-Based Agents
  5. Method
  6. Overview of the AI2Agent Framework
  7. Guideline-Driven Execution
  8. Self-Adaptive Debug
  9. Step-by-Step Execution
  10. Environment-Aware Debug
  11. Knowledge-Guided Refinement
  12. Case & Solution Accumulation
  13. Retrieval of Deployment Insights
  14. Continuous Learning and Refinement
  15. Case Study
  16. ...and 4 more sections

Figures (5)

  • Figure 1: Left: The AI2Agent user interface, illustrating the automated workflow where a user request (e.g., generating a talk show in a specific style) initiates a structured execution process. This includes searching for a suitable project, following the predefined guidelines for execution, auto-deployment and debug to ensure success. Right: A conceptual visualization of local auto-deployment and Agent auto-package, demonstrating how AI2Agent transforms text-to-speech(TTS) functionality into a fully autonomous agent.
  • Figure 2: Comparison of Paradigms. DevOps relies on manual YAML configuration and CI/CD workflows with manual debug. AutoDevOps offers semi-automated configuration but still requires human intervention. AI2Agent achieves end-to-end automated performance, including guideline-driven execution, self-adaptive debug, and case & solution accumulation.
  • Figure 3: Screenshot of our local auto-deployment process. Left: Execution following predefined guidelines to ensure structured and reliable deployment. Right: The inference and planning interface for dynamically adapting to deployment conditions.
  • Figure 4: Screenshot of the user interface after auto-deployment.
  • Figure 5: Comparison of manual vs. AI2Agent deployment: (1) Time Consumption: AI2Agent reduces deployment time by 78%. (2) Success Rate: AI2Agent improves success rate by 48%.